def train(self):
print("Total number of parameters: %d" % (self.hyp.shape[0]))
# Gradients from autograd
NLML = value_and_grad(self.likelihood)
start_time = timeit.default_timer()
for i in range(1, self.max_iter + 1):
# Fetch minibatch
self.X_batch, self.y_batch = fetch_minibatch(
self.X, self.y, self.N_batch)
# Compute likelihood and gradients
nlml, D_NLML = NLML(self.hyp)
# Update hyper-parameters
self.hyp, self.mt_hyp, self.vt_hyp = stochastic_update_Adam(
self.hyp, D_NLML, self.mt_hyp, self.vt_hyp, self.lrate, i)
if i % self.monitor_likelihood == 0:
elapsed = timeit.default_timer() - start_time
print('Iteration: %d, NLML: %.2f, Time: %.2f' %
(i, nlml, elapsed))
start_time = timeit.default_timer()
nlml, D_NLML = NLML(self.hyp)
def train(self):
# Gradients from autograd
MSE = value_and_grad(self.MSE)
for i in range(1,self.max_iter+1):
# Fetch minibatch
self.X_batch, self.Y_batch = fetch_minibatch(self.X, self.Y, self.N_batch)
# Compute MSE and gradients
MSE_value, D_MSE = MSE(self.hyp)
# Update hyper-parameters
self.hyp, self.mt_hyp, self.vt_hyp = stochastic_update_Adam(self.hyp, D_MSE, self.mt_hyp, self.vt_hyp, self.lrate, i)
if i % self.monitor_likelihood == 0:
print("Iteration: %d, MSE: %.5e" % (i, MSE_value))
def train(self):
# Gradients from autograd
NLML = value_and_grad(self.likelihood)
for i in range(1,self.max_iter+1):
# Fetch minibatch
self.X_batch, self.Y_batch = fetch_minibatch(self.X, self.Y, self.N_batch)
# Compute likelihood_UB and gradients
NLML_value, D_NLML = NLML(self.hyp)
# Update hyper-parameters
self.hyp, self.mt_hyp, self.vt_hyp = stochastic_update_Adam(self.hyp, D_NLML, self.mt_hyp, self.vt_hyp, self.lrate, i)
if i % self.monitor_likelihood == 0:
print("Iteration: %d, likelihood: %.2f" % (i, NLML_value))
def train():
X = ModelInfo["X"]
y = ModelInfo["y"]
init_params()
max_iter = ModelInfo["max_iter"]
N_batch = ModelInfo["N_batch"]
hyp = ModelInfo["hyp"]
monitor_likelihood = ModelInfo["monitor_likelihood"]
# Adam optimizer parameters
mt_hyp = np.zeros(hyp.shape)
vt_hyp = np.zeros(hyp.shape)
lrate = ModelInfo["lrate"]
print("Total number of parameters: %d" % (hyp.shape[0]))
# Gradients from autograd
UB = value_and_grad(likelihood_UB)
start = time.time()
for i in range(1, max_iter + 1):
# Fetch minibatch
X_batch, y_batch = fetch_minibatch(X, y, N_batch)
ModelInfo.update({"X_batch": X_batch})
ModelInfo.update({"y_batch": y_batch})
# Compute likelihood_UB and gradients
hyp = ModelInfo["hyp"]
NLML, D_NLML = UB(hyp)
# Update hyper-parameters
hyp, mt_hyp, vt_hyp = stochastic_update_Adam(hyp, D_NLML, mt_hyp,
vt_hyp, lrate, i)
ModelInfo.update({"hyp": hyp})
if i % monitor_likelihood == 0:
end = time.time()
print(
"Iteration: %d, likelihood_UB: %.2f, elapsed time: %.2f seconds"
% (i, NLML, end - start))
start = time.time()
NLML, D_NLML = UB(hyp)